Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI
Multiple-timepoint arterial spin labelling MRI is a non-invasive imaging technique that permits measurement of both cerebral blood flow and arterial transit time, the latter of which is an emerging biomarker of interest for cerebrovascular health. Quantification of arterial spin labelling data is ch...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1536752/full |
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| author | Thomas F. Kirk Thomas F. Kirk Georgia G. Kenyon Georgia G. Kenyon Martin S. Craig Martin S. Craig Michael A. Chappell Michael A. Chappell |
| author_facet | Thomas F. Kirk Thomas F. Kirk Georgia G. Kenyon Georgia G. Kenyon Martin S. Craig Martin S. Craig Michael A. Chappell Michael A. Chappell |
| author_sort | Thomas F. Kirk |
| collection | DOAJ |
| description | Multiple-timepoint arterial spin labelling MRI is a non-invasive imaging technique that permits measurement of both cerebral blood flow and arterial transit time, the latter of which is an emerging biomarker of interest for cerebrovascular health. Quantification of arterial spin labelling data is challenging due to the low signal to noise ratio and non-linear tracer kinetics of this technique. In this work, we introduce a new quantification method called SSVB that addresses limitations in existing methods and demonstrate its performance using simulations and acquisition data. Simulations showed that the method is more accurate, particularly for estimating arterial transit time, and more robust to noise than existing techniques. On high spatial resolution data acquired at 3 T, the method produced less noisy parameter maps than the comparator method and captured greater variation in arterial transit time on a cross-sectional cohort. |
| format | Article |
| id | doaj-art-842b1ac88f1b4d3ab9a45fd0dd1fa6c3 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-842b1ac88f1b4d3ab9a45fd0dd1fa6c32025-02-04T06:32:05ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-02-011910.3389/fnins.2025.15367521536752Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRIThomas F. Kirk0Thomas F. Kirk1Georgia G. Kenyon2Georgia G. Kenyon3Martin S. Craig4Martin S. Craig5Michael A. Chappell6Michael A. Chappell7Quantified Imaging Limited, London, United KingdomSir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United KingdomSir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United KingdomSchool of Computer and Mathematical Sciences, University of Adelaide, Adelaide, SA, AustraliaQuantified Imaging Limited, London, United KingdomSir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United KingdomQuantified Imaging Limited, London, United KingdomSir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United KingdomMultiple-timepoint arterial spin labelling MRI is a non-invasive imaging technique that permits measurement of both cerebral blood flow and arterial transit time, the latter of which is an emerging biomarker of interest for cerebrovascular health. Quantification of arterial spin labelling data is challenging due to the low signal to noise ratio and non-linear tracer kinetics of this technique. In this work, we introduce a new quantification method called SSVB that addresses limitations in existing methods and demonstrate its performance using simulations and acquisition data. Simulations showed that the method is more accurate, particularly for estimating arterial transit time, and more robust to noise than existing techniques. On high spatial resolution data acquired at 3 T, the method produced less noisy parameter maps than the comparator method and captured greater variation in arterial transit time on a cross-sectional cohort.https://www.frontiersin.org/articles/10.3389/fnins.2025.1536752/fullperfusionarterial transit time (ATT)arterial spin label (ASL) MRIcerebral blood flow (CBF)quantification |
| spellingShingle | Thomas F. Kirk Thomas F. Kirk Georgia G. Kenyon Georgia G. Kenyon Martin S. Craig Martin S. Craig Michael A. Chappell Michael A. Chappell Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI Frontiers in Neuroscience perfusion arterial transit time (ATT) arterial spin label (ASL) MRI cerebral blood flow (CBF) quantification |
| title | Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI |
| title_full | Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI |
| title_fullStr | Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI |
| title_full_unstemmed | Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI |
| title_short | Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI |
| title_sort | stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion mri |
| topic | perfusion arterial transit time (ATT) arterial spin label (ASL) MRI cerebral blood flow (CBF) quantification |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1536752/full |
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